ZUNA (braindecode re-host)
Faithful re-host of the ZUNA EEG foundation-model encoder weights for use with braindecode.
- Original model:
Zyphra/ZUNA - Original code: https://github.com/Zyphra/zuna
- Paper: Warner, C., Mago, J., Huml, J.R., Osman, M. and Millidge, B. (2026). ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders. arXiv:2602.18478
- Original authors (Zyphra): Chris Warner, Jonas Mago, Jon Huml, et al.
- License: Apache-2.0 (inherited from the upstream release)
Why this re-host
The braindecode ZUNA port loads these weights through
ZUNA.from_pretrained(...). Re-hosting under the braindecode org gives a
stable, permanent location that the library can point to by default, so the
integration does not depend on the upstream repository staying unchanged. The
weights file is bit-identical to the upstream checkpoint (same SHA-256);
only the filename is normalised to the standard model.safetensors.
What is (and is not) pretrained
These are the pretrained encoder weights (a position-aware diffusion autoencoder trained for EEG superresolution). The braindecode wrapper adds a classification head that is randomly initialised and must be fine-tuned on your downstream task — loading these weights alone does not give a trained classifier.
Usage
from braindecode.models import ZUNA
# Defaults to this repo (braindecode/ZUNA); n_chans / n_outputs are montage-
# and task-dependent and must be supplied.
model = ZUNA.from_pretrained(n_chans=19, n_outputs=4)
# Inputs are 5 s EEG windows sampled at 256 Hz (n_times = 1280).